Inferring metabolic networks using the bayesian adaptive graphical lasso with informative priors

Statistics and its Interface

Volumn 6, Issue 4, 2013, Pages 547-558

  Peterson, Christine ^a   Vannucci, Marina ^a   Karakas, Cemal ^b   Choi, William ^b   Ma, Lihua ^b   Maletić Savatić, Mirjana ^b  

^a Rice University   (United States)

^b TEXAS CHILDREN S HOSPITAL   (United States)

Author keywords

Bayesian adaptive graphical lasso; Graphical models; Informative prior; Metabolic network; Neuroinflammation

Indexed keywords

EID: 84893359865     PISSN: 19387989     EISSN: 19387997     Source Type: Journal    
DOI: 10.4310/SII.2013.v6.n4.a12     Document Type: Article

References (54)

1. Amores-Sánchez, M. and Ángel-Medina, M. (1999). Glutamine, as a precursor of glutathione, and oxidative stress. Mol. Genet. Metab. 67 100-105.
2. Banati, R. (2003). Neuropathological imaging: in vivo detection of glial activation as a measure of disease and adaptive change in the brain. Br. Med. Bull. 65(1) 121-131.
3. Barabási, A. and Albert, R. (1999). Emergence of scaling in random networks. Science 286 509-512. MR2091634
4. Bang, J., Crockford, D., Holmes, E., Pazos, F., Sternberg, M., Muggleton, S. and Nicholson, J. (2008). Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods. J. Proteome Res. 7 497-503.
5. Bornn, L., Gottardo, R. and Doucet, A. (2010). Grouping Priors and the Bayesian Elastic Net. Technical report, Department of Statistics, University of British Columbia.
6. Camacho, D., de la Fuenta, A. and Mendes, P. (2005). The origin of correlations in metabolomics data. Metabolomics 1(1) 53-63.
7. Cooper, A. (2012). The role of glutamine synthetase and glutamate dehydrogenase in cerebral ammonia homeostasis. Neurochem. Res. http://dx.doi.org/10.1007/s11064-012-0803-4.
8. Coen, M., Holmes, E., Lindon, J. and Nicholson, J. (2008). NMR-based metabolic profiling and metabonomic approaches to problems in molecular toxicology. Chem. Res. Toxicol. 21(1) 9- 27.
9. Davis, V., Bathe, O., Schiller, D., Slupsky, C. and Sawyer, M. (2011). Metabolomics and surgical oncology: Potential role for small molecule biomarkers. J. Surg. Onc. 103(5) 451- 459.
10. Dunn, W., Bailey, N. and Johnson, H. (2005). Measuring the metabolome: Current analytical technologies. Analyst 130(5) 606-625.
11. Doring, A. and Yong, V. (2011). The good, the bad and the ugly. Macrophages/microglia with a focus on myelin repair. Front. Biosci. (Schol. Ed.) 3 846-856.
12. Fiehn, O. (2002). Metabolomics - the link between genotypes and phenotpyes. Plant Mol. Biol. 48(1) 155-171.
13. Csardi, G. and Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.
14. Ebbels, T. and Cavill, R. (2007). Bioinformatic methods in NMR-based metabolic profiling. Prog. Nucl. Mag. Res. Sp. 55(4) 361-374.
15. Ebbels, T., Lindon, J. and Coen, M. (2011). Processing and modeling of nuclear magnetic resonance (NMR) metabolic profiles. Methods Mol. Biol. 708 365-388.
16. Fan, J., Feng, Y. and Wu, Y. (2009). Network exploration via the adaptive lasso and SCAD penalties. Ann. Appl. Stat. 3(2) 521-541. MR2750671
17. Friedman, J., Hastie, T. and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics 9(3) 432-441.
18. Genius, J., Geiger, J., Bender, A., Möller, H., Klopstock, T. and Rujescu, D. (2012). Creatine protects against excitotoxicity in an in vitro model of neurodegeneration. PLoS One 7(2) e30554.
19. Griffin, J. and Brown, P. (2007). Bayesian adaptive Lassos with non-convex penalization. Technical report, University of Kent.
20. Hanisch, U. (2002). Microglia as a source and target of cytokines. Glia 40(2) 140-155.
21. Hoarau, J., Krejbich-Trotot, P., Jaffar-Bandjee, M., Das, T., Thon-Hon, G., Kumar, S., Neal, J. and Gasque, P. (2011). Activation and control of CNS innate immune responses in health and diseases: A balancing act finely tuned by neuroimmune regulators (NIReg). CNS Neurol. Disord. Drug Targets 10(1) 25- 43.
22. Holmes, E., Wilson, I. and Nicholson, J. (2008). Metabolic phenotyping in health and disease. Cell 134(5) 714-717.
23. Jeong, H., Tombor, B., Albert, R., Oltvai, Z. and Barabási, A. (2000). The large-scale organization of metabolic networks. Nature 407(6804) 651-654.
24. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. and Tanabe, M. (2012). KEGG for integration and interpretation of largescale molecular datasets. Nucleic Acids Res. 40 D109-D114.
25. Kettenmann, H., Hanisch, U., Noda, M., and Verkhratsky, A. (2011). Physiology of microglia. Physio. Rev. 91 461-553.
26. Kreutzberg, G. (1996). Microglia: A sensor for pathological events in the CNS. Trends Neurosci. 19(8) 312-318.
27. Krumsiek, J., Suhre, K., Illig, T., Adamski, J. and Theis, F. (2011). Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data. BMC Syst. Biol. 5(21) 1-16.
28. Lauritzen, S. (1996). Graphical Models. Clarendon Press, Oxford. MR1419991
29. Lee, J., Tran, T. and Tansey, M. (2009) Neuroinflammation in Parkinson's disease. J. Neuroimmune Pharmacol. 4(4) 419-429.
30. Li, Q., and Lin, N. (2010). The Bayesian elastic net. Bayesian Anal. 5(1) 151-170. MR2596439
31. Li, X., Song, L. and Jope, R. (1998). Glutathione depletion exacerbates impairment by oxidative stress of phosphoinositide hydrolysis, AP-1, and NF-κB activation by cholinergic stimulation. Brain Res. Mol. Brain Res. 53 196-205.
32. Liu, F., Chakraborty, S., Fan, L. and Liu, Y. (2012). Bayesian regularization via the graph Laplacian prior. 1- 31. http://www.stat.duke.edu/~fl35/APT/topten papers/glJASA11submit.pdf.
33. Lunt, S. and Vander Heiden, M. (2011). Aerobic glycolysis: Meeting the metabolic requirements of cell proliferation Annu. Rev. Cell Dev. Bi. 27 441-464.
34. Meinshausen, N. and Bühlmann, P. (2006). High-dimensional graphs and variable selection with the Lasso. Ann. Statist. 34(3) 1436-1462. MR2278363
35. Mémet, S. (2006). NF-κB functions in the nervous system: From development to disease. Biochem. Pharmacol. 72 1180-1195.
36. Metz, T. (2011). Metabolic Profiling: Methods and Protocols Springer, New York.
37. Meyer, U., Feldon, J. and Dammann, O. (2011). Schizophrenia and autism: both shared and disorder-specific pathogenesis via perinatal inflammation? Pediatr. Res. 69(5) 26R-33R.
38. Nicholson, J., Connelly, J., Lindon, E. and Holmes, E. (2002). Metabonomics: A platform for studying drug toxicity and gene function. Nat. Rev. Drug Discov. 1 153-161.
39. Park, T., and Casella, G. (2008). The Bayesian lasso. J. Am. Stat. Assoc. 103(482) 681-686. MR2524001
40. Persson, M., Brantefjord, M., Hansson, E. and Rönnbäck, L. (2005). Lipopolysaccharide increases microglial GLT-1 expression and glutamate uptake capacity in vitro by a mechanism dependent on TNF-a. Glia 51 111-120.
41. Peterson, L. and Fujinami, R. (2007). Inflammation, demyelination, neurodegeneration and neuroprotection in the pathogenesis of multiple sclerosis. J. Neuroimmunol. 184(1-2) 37-44.
42. Possel, H., Noack, H., Putzke, J., Wolf, G. and Sies, H. (2000). Selective upregulation of inducible nitric oxide synthase (iNOS) by lipopolysaccharide (LPS) and cytokines in microglia: in vitro and in vivo studies. Glia 32 51-59.
43. Russo, C., Lisi, L., Tringali, G. and Navarra, P. (2009). Involvement of mTOR kinase in cytokine-dependent microglial activation and cell proliferation. Biochem. Pharmacol. 78 1242-1251.
44. Steuer, R., Kurths, J., Fiehn, O. and Weckwerth, W. (2003). Observing and interpreting correlations in metabolic networks. Bioinformatics 19(8) 1019-1026.
45. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. J. R. Statist. Soc. B 58(1) 267-288. MR1379242
46. Tokunga, C., Yoshino, K. and Yonezawa, K. (2004). mTOR integrates amino acid- and energy-sensing pathways. Biochem. Bioph. Res. Co. 313 443-446.
47. Wang, H. (2012). Bayesian graphical lasso models and efficient posterior computation. Bayesian Analysis 7(2) 771-790.
48. Weljie, A., Newton, J., Mercier, P., Carlson, E. and Slupsky, C. (2006). Targeted profiling: Quantitative analysis of 1HNMR metabolomics data. Anal. Chem. 78 4430-4442.
49. Wyss, M., and Kaddurah-Daouk, R. (2000). Creatine and creatinine metabolism. Physio. Rev. 83(3) 1107-1213.
50. Yang, L., Tanaka, J., Zhang, B., Sakanaka, M. and Maeda, N. (1998). Astrocytes modulate nitric oxide production by microglial cells through secretion of serine and glycine. Biochem. Bioph. Res. Co. 251 277-282.
51. Yuan, M. and Lin, Y. (2007). Model selection and estimation in the Gaussian graphical model. Biometrika 94(1) 19-35. MR2367824
52. Zhang, J. and Wiemann, S. (2009). KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor. Bioinformatics 25(11) 1470-1471.
53. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J. R. Statist. Soc. B 67(Part 2) 301-320. MR2137327
54. Zou, J. and Crews, F. (2005). TNFa potentiates glutamate neurotoxicity by inhibiting glutamate uptake in organotypic brain slice cultures: Neuroprotection by NF-κB inhibition. Brain Res. 1034 11-24.